Field of Invention
Various embodiments of the present disclosure relate to a building sensing management method and a driving method thereof, and more particularly, to a building sensing management system that is capable of minimizing the amount of data being transmitted from sensors, and a driving method thereof.
Description of Related Art
Thanks to the rapid development of science, technologies and societies, the number of buildings is increasing worldwide, and these buildings are developing into pleasant and comfortable places where people can rest and work.
Recently, buildings are being integrated with IT, and are being designed for automation and security purpose using electricity, lighting, wireless sensors and the like. Furthermore, with the development of wireless network, various services are being provided inside the buildings using wireless network infrastructure.
Meanwhile, numerous sensors are being installed inside a building. These sensors may provide data of the places where they are installed (for example, environment information) to a controller. Then, using the data from the sensors, the controller may control the environment inside the building. Herein, the numerous sensors generally transmit the data periodically. Such periodical transmission of environment information secures the reliability of the data being transmitted. And when there are just a small number of sensors, a small amount of data will be transmitted, which will lessen the burden on the network.
When the sensors transmit data periodically, however, the traffic of the network will increase exponentially in response to an increase of the number of the sensors, which is a problem. Therefore, in recent days, a dual prediction scheme is being used when transmitting and receiving data. The dual prediction scheme refers to a scheme where a sensor (sensor node) that transmits information and a controller (sync node) that receives information use the same prediction algorithm.
In terms that an error of a sensing value is allowable to some degree, the dual prediction scheme is a different method from synchronization which synchronizes a sensor value precisely. Such a dual prediction scheme is capable of significantly reducing the amount of traffic of a network due to this allowable error (threshold).
Examples of the dual prediction scheme include Constant Measurement (hereinafter referred to as “CM”), LMS (Least Mean Square), RLS (Recursive Least Square), AR (AutoRegression), ARMA (AutoRegression Moving Average), ES (Exponentially Smoothing), and Dual Kalman Filter and the like, and of these methods, the CM method is mostly generally used.
In the CM method, a constant error range is predetermined, and if a measured value is not outside the predetermined error range, the measured value is not transceived. That is, in the CM method, the measured data is transmitted only when it is outside the error range, and therefore the amount of data transmission may be reduced to some extent.
However, in the CM method, the amount of data transmission may increase significantly when the environment is changing continuously. For example, in the CM method, a sensing value changes in a sinewave format, and if the error range is smaller than the amplitude of a sinewave, the sensed data must be transmitted at least every half the period of the sinewave.